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Record W3170067248 · doi:10.6000/1929-4409.2021.10.114

Disconnectivity Social of Conflict in the Circle of Iron Ore Mine in Bone Regency, South Sulawesi, Indonesia

2021· article· en· W3170067248 on OpenAlexvenueno aff
Andi Ilham Samanlangi, Andi Agustang, Arlin Adam, Andi Alim

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Education and Local Wisdom
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodDisconnectionGovernment (linguistics)JealousySocial conflictWelfareSocial WelfareQualitative researchBusinessDevelopment economicsEconomic growthAgriculturePolitical scienceEconomicsGeographyPoliticsSociologySocial scienceLawPsychologySocial psychology

Abstract

fetched live from OpenAlex

Conflict is a common occurrence in mining activities that ignore the triple bottom line principle. Neglecting the impact of environmental damage and the welfare of the communities around the mine has resulted in social disconnection leading to conflict. This study aims to describe comprehensively the social disconnect in the iron ore mining project in Bontocani District so that mining conflicts do not occur. The research method used is a mixed-method with a sequential design starting with qualitative methods then followed by quantitative methods. The findings of this study indicate that the causes of social disconnect include: There are environmental impacts that damage the livelihoods of residents around the mine area, the company does not respect local wisdom, the role of government is not maximal as a mediator, The economic impact is in the form of loss of livelihoods in the agricultural sector which is exacerbated by road infrastructure. damaged and Social impact in the form of jealousy between residents. The practical implication of this research is the creation of a harmonious relationship between the government, companies and communities in mining management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.083
GPT teacher head0.382
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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